Instructions to use dzungpham/graphcodebert-code-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dzungpham/graphcodebert-code-classification with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("dzungpham/graphcodebert-code-classification", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 975fa0049130801545ab4c609f09a7b9fd2011d057ed1d4b16a349e9116177c8
- Size of remote file:
- 499 MB
- SHA256:
- 7a01766ea37053c4e1086db23a592ccd390b6f66d530273ae2dae69fbf9aa39e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.